Neural-Symbolic Cognitive ReasoningHumans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This book is the first to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics. By using a graphical presentation, it explains neural networks through a sound neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capabilities. The book will be invaluable reading for academic researchers, graduate students, and senior undergraduates in computer science, artificial intelligence, machine learning, cognitive science and engineering. It will also be of interest to computational logicians, and professional specialists on applications of cognitive, hybrid and artificial intelligence systems. |
Contents
Introduction | 1 |
Logic and Knowledge Representation | 9 |
Artificial Neural Networks 23 | 22 |
NeuralSymbolic Learning Systems | 35 |
Connectionist Modal Logic | 55 |
Connectionist Temporal Reasoning | 75 |
Connectionist Intuitionistic Reasoning 87 | 86 |
Applications of Connectionist Nonclassical Reasoning | 101 |
Other editions - View all
Neural-Symbolic Cognitive Reasoning Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay Limited preview - 2008 |
Neural-Symbolic Cognitive Reasoning Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay No preview available - 2010 |